AI discussions on Twitter and the role of academic radiologists made headlines this week.

This week’s articles in Diagnostic Reading include: community-based academic radiologists may improve care; factors that lead to missed injuries in CT scans; 4 reasons for moving your healthcare PACS to the cloud; what tweets say about AI in radiology; and radiologists are reading more lower extremity MRIs than nonradiologists.

Community-based academic radiologists—hybrid practitioners who focus on both academia and community health—could be key in improving access to specialized care, according to a recent report in the Journal of the American College of Radiology. Academic radiologists are critical to the growth of the specialty across the country, the authors stated.

In patients with multiple traumas, those who were over 30 years old with severe injuries across more than two body parts were more likely to experience a missed injury upon initial CT scan, according to a recent Radiology study. Research on the topic has yielded varying missed injury rates—ranging from 3.5 percent to 71.4 percent—and these misses can result in grave consequences for all involved, noted the authors. Read the blog on 15 strategies to help minimize radiological errors in MRI, CT, and ultrasound imaging.

Artificial intelligence (AI) is an immensely popular topic in radiology, sparking countless discussions and debates about whether it will give radiologists a new tool for providing high-quality patient care or end up replacing them altogether. This article highlights three key discussions about AI in radiology gathered from the popular social media platform Twitter—instead of academic journals and medical conferences.

Nonradiologists such as orthopedic surgeons are interpreting a growing number of musculoskeletal imaging examinations, according to a study recently published in the American Journal of Roentgenology. However, radiologists are still performing a lion’s share of the work, including a much higher percentage of weekend reads and clinically complex patients.

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